Building a common description of land cover in a tropical watershed plagued with intercultural conflicts: The value of participatory 3D modelling
Bibliographic record
Abstract
Natural forest management and conservation projects such as reducing emissions from deforestation and forest degradation, and enhancing forest carbon stocks (REDD+) face many challenges in the field. Implementation of these projects depends on such factors as clarity of information among stakeholders, legal security of territories, and local decision-making power. These challenges have been previously identified in the Upper Bayano watershed of eastern Panama, where a long history of land cover and land-use conflicts is present between three different human groups. With a long-term objective of natural forest conservation, this study aims to develop and test participatory approaches (participatory mapping and participatory 3D modelling) for the Upper Bayano watershed in an attempt to create a consensus among all stakeholders on current land cover and land-use conflicts to overcome challenges faced by projects as REDD+. We found that the third dimension allows a common understanding over the landscape, creates a common ground discussion, and leads towards a consensus, while the participatory approach brings discussion and positive effects among the stakeholders and the bridging institutions bring equity and transparency. Finally, we discuss implications of this knowledge generation and common agreement over the landscape for future forest management projects such as REDD+’s implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".